The invention relates generally to radiography and more particularly to a method and system for automatically identifying defects from a radiographic image of a scanned object.
Radiography is a technique of producing an image of any opaque object by the penetration of radiation, such as gamma rays, X-rays, neutrons, or charged particles. When a beam of radiation is transmitted through any object, the radiation is differentially absorbed depending upon variations in object thickness, density, and chemical composition. The energy emergent from the object forms a radiographic image, which may then be realized on an image detection medium, such as a radiation sensitive detector. The detector comprises an array of elements that records the number of incident photons at each element position, and maps the recording onto a two-dimensional (2D) image. The 2D image is then fed to a computer workstation and interpreted by trained personnel.
Radiography finds wide application in various medical and industrial applications as a non-destructive technique for examining the internal structure of an object. For example, in aerospace and automotive industries, radiographic images of aluminum castings are typically inspected by an operator who identifies defects pertaining to porosities, inclusions, shrinkages, cracks, etc. in the castings. However, and as will be appreciated by those skilled in the art, owing to the structural complexity and large production volumes of these castings, the manual inspection procedure is often prone to operator fatigue and hence suffers from low inspection reliability.
A number of radiographic inspection techniques such as feature-based classification, artificial neural networks and adaptive filtering have been developed to perform radiographic inspections of scanned objects. Feature-based classification techniques evaluate a set of features to identify potential flaws in scanned object parts based on flaw morphology and gray level statistics. These techniques assign each pixel in the image into one of several classes based on minimizing a distance metric, wherein the parameters characterizing the distance metric are evaluated using a supervised learning scheme. However, the performance of these techniques is affected by variations caused by object structure or flaw morphology and these techniques generally require large training sets with labeled flaws to perform defect identification.
It would be desirable to develop a radiographic inspection technique that automatically identifies defects from radiographic images of scanned objects. In addition, it would be desirable to develop an efficient radiographic inspection technique that produces accurate defect detectability rates, efficiently utilizes system operation setup time and system training time and is robust to changes in object part geometry and misalignment of scanned object parts.